Kai-Po Chang

Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease associated with extraintestinal manifestations, including primary sclerosing cholangitis (PSC). Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease that rarely coexists with UC or PSC. The concurrent occurrence of UC, PSC, and SLE in a single individual represents a unique diagnostic and therapeutic challenge. Vedolizumab, a gut-selective biologic agent, is effective for managing UC; however, its utility in patients presenting with this triad of conditions has not yet been explored. Case summary: A 32-year-old man presented with a 10-year history of recurrent upper abdominal pain, frequently accompanied by high-grade fever, along with recent onset of jaundice, diarrhea, hematochezia, and chronic rashes. Diagnostic evaluation confirmed PSC, SLE, and severe UC. During hospitalization, the patient also developed bacteremia. Initial management of UC with mesalazine and immunosuppressants (azathioprine followed by cyclosporine) resulted in limited clinical improvement. Vedolizumab was subsequently initiated, resulting in marked clinical improvements and near-complete endoscopic remission of UC. PSC and SLE remained clinically stable with ongoing therapies; however, the patient is currently awaiting liver transplantation for PSC. Conclusions: This case highlights the potential utility of vedolizumab in the treatment of UC in patients with concurrent PSC and SLE.

Background/Objectives: Free-text surgical pathology reports hinder automated cancer registry entry and secondary analytics. This study introduces a clinically governed schema layer for interoperability, testing whether a locally-deployable Large Language Model (LLM) pipeline can deliver robust registry-grade extraction across institutions. Methods: We developed a College of American Pathologists (CAP)-aligned clinical ontology encompassing 10 cancer types, 192 per-organ scalar fields, key biomarkers, and nested structures for lymph nodes and margins. Encoded via Declarative Self-improving Python (DSPy) signatures with grammar-constrained decoding using DSPy v3.2.1, this model-agnostic pipeline was benchmarked on 893 internal reports against a pathologist-adjudicated gold standard. External validation utilized 242 The Cancer Genome Atlas (TCGA) reports. Hardware feasibility was confirmed on a single 48-gigabyte (GB) Graphics Processing Unit (GPU), ensuring suitability for privacy-preserving on-premises deployment. Results: Using the gpt-oss-20b model, the framework achieved 92.0% macro-mean exact-match accuracy on internal data, demonstrating near-perfect run-to-run reliability. Critical prognostic indicators, including breast estrogen receptor/progesterone receptor (ER/PR) (98.7%) and margin positivity (>93%), maintained high fidelity. On the external TCGA cohort, accuracy was 77.5%, rising to 88.0% after excluding structurally silent fields absent in older narratives. Operationally, the model processed reports in 40–70 s, optimally balancing speed and accuracy. Conclusions: This schema-first abstraction layer successfully decouples clinical logic from specific Artificial Intelligence (AI) models. By reliably transforming narrative reports into machine-readable structures, it establishes a portable privacy-preserving foundation for automated cancer surveillance, institutional data reuse, and future multimodal clinical systems.

Background/Objectives: Free-text surgical pathology reports hinder automated cancer registry entry and secondary analytics. This study introduces a clinically governed schema layer for interoperability, testing whether a locally-deployable Large Language Model (LLM) pipeline can deliver robust, registry-grade extraction across institutions. Methods: We developed a College of American Pathologists (CAP)-aligned clinical ontology encompassing 10 cancer types, 192 per-organ scalar fields, key biomarkers, and nested structures for lymph nodes and margins. Encoded via Declarative Self-improving Python (DSPy) signatures with grammar-constrained decoding, this model-agnostic pipeline was benchmarked on 893 internal reports against a pathologist-adjudicated gold standard. External validation utilized 242 The Cancer Genome Atlas (TCGA) reports. Hardware feasibility was confirmed on a single 48-gigabyte (GB) Graphics Processing Unit (GPU), ensuring suitability for privacy-preserving, on-premise deployment. Results: Using the gpt-oss-20b model, the framework achieved 92.0% macro-mean exact-match accuracy on internal data, demonstrating near-perfect run-to-run reliability. Critical prognostic indicators, including breast estrogen receptor/progesterone receptor (ER/PR) (98.7%) and margin positivity (>93%), maintained high fidelity. On the external TCGA cohort, accuracy was 77.5%, rising to 88.0% after excluding structurally silent fields absent in older narratives. Operationally, the model processed reports in 40-70 seconds, optimally balancing speed and accuracy. Conclusions: This schema-first abstraction layer successfully decouples clinical logic from specific Artificial Intelligence (Al) models. By reliably transforming narrative reports into machine-readable structures, it establishes a portable, privacy-preserving foundation for automated cancer surveillance, institutional data reuse, and future multimodal clinical systems.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions about treatment strategies or the use of palliative care. (2) Methods: Gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected a total of 1187 RNA-seq data points from breast cancer patients (median age 58 years) in Fragments Per Kilobase Million (FPKM) format from the TCGA database. Among them, we selected 144 patients with date of death information to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to TPM to build the survival prediction model SaBrcada. After normalization and dimension raising, we used the differential gene expression data to test eight different deep learning architectures. Considering the effect of age on prognosis, we also performed a stratified random sampling test on all ages between the lower and upper quartiles of patient age, 48 and 69 years; (3) Results: Stratifying by age 61, the performance of SaBrcada built by GoogLeNet was improved to a highest accuracy of 0.798. We also built a free website tool to provide five predicted survival periods: within six months, six months to one year, one to three years, three to five years, or over five years, for clinician reference. (4) Conclusions: We built the prediction model, SaBrcada, and the website tool of the same name for breast cancer survival analysis. Through these models and tools, clinicians will be provided with survival interval information as a basis for formulating precision medicine.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions on treatment strategies or the use of palliative care for patients; (2) Methods: The gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected the RNA-seq data of breast cancer patients, a total of 1187 RNA-seq data (median age 58 years), in FPKM format from the TCGA database. Among them, 144 RNA-seq data with date of death information was selected to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to transcripts per million (TPM) to build survival prediction model SaBrcada. After normalization and dimension raising, the differential gene expression data were used for testing eight different deep learning architectures. Among them, GoogLeNet performed the best. Considering the effect of age on prognosis, we examined all ages between the lower and upper quartiles of patient age for a stratified random sampling test; (3) Results: Stratifying by age based on a cut-off of 61 years of age improved the accuracy of SaBrcada compared to previous findings, resulting in an accuracy of 0.798. We also built a free website tool to provide 5 kinds of predicted survival period information for clinician reference; (4) Conclusions: We established a breast cancer survival analysis prediction model, SaBrcada, and a website tool with the same name. Through this highly reliable survival analysis model and website tool, information on survival intervals will be provided for clinicians as part of precision medicine.

The impact of COVID-19 on the human body after infection requires further investigation. The protease carried by the virus may interact with cellular proteins, thereby affecting the regulatory pathways. Machine learning can provide predictive information to aid the research design in such studies. This study examined the effectiveness of different encoding methods for training models using traditional machine learning and deep learning methods. Using the advantages of deep learning methods for high-dimensional data analysis, amino acid properties were extracted and classified. Viral polyprotein data were used as the training set and human protein substrates were collected from the literature as the independent test set. In the one-dimensional convolutional neural network model, the accuracy of the independent test set was the highest, at 0.832.

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodies can help with vaccine production and food allergen detection in patients. Many studies have focused on predicting linear B-cell epitopes, but only two prediction tools are currently available to predict the sub-type of an epitope. NIgPred was developed as a prediction tool for IgA, IgE, and IgG. NIgPred integrates various heterologous features with machine-learning approaches. Differently from previous studies, our study considered peptide-characteristic correlation and autocorrelation features. Sixty kinds of classifier were applied to construct the best prediction model. Furthermore, the genetic algorithm and hill-climbing algorithm were used to select the most suitable features for improving the accuracy and reducing the time complexity of the training model. NIgPred was found to be superior to the currently available tools for predicting IgE epitopes and IgG epitopes on independent test sets. Moreover, NIgPred achieved a prediction accuracy of 100% for the IgG epitopes of a coronavirus data set. NIgPred is publicly available at our website.

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance.

Information about the expression status of hormone receptors such as estrogen receptor (ER), progesterone receptor (PR), and Her‐2 is crucial in the management and prognosis of breast cancer. Therefore, the retrieval and analysis of hormone receptor expression characteristics in metastatic breast cancer may be valuable in breast cancer study. Herein, we report a text mining tool based on word/phrase matching that retrieves hormone receptor expression data of regional or distant metastatic breast cancer from pathology reports. It was tested on pathology reports at the China Medical University Hospital from 2013 to 2018. The tool showed specificities of 91.6% and 63.3% for the detection of regional lymph node metastasis and distant metastasis, respectively. Sensitivity in immunohistochemical study result extraction in these cases was 98.6% for distant metastasis and 78.3% for regional lymph node metastasis. Statistical analysis on these retrieved data showed significant difference s in PR and Her‐2 expressions between regional and metastatic breast cancer, which is compatible with previous studies. In conclusion, our study shows that metastatic breast cancer hormone receptor expression characteristics can be retrieved by text mining. The algorithm designed in this study may be useful in future studies about text mining in pathology reports.